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Journal of Intelligent Computing

Simulation Experiment on Optimization of New Energy Customer Value Model Based on Genetic algorithm

Zhang Xiaoping
Chengdu University of Technology Chengdu, 610000, Sichuan China

Abstract: With the rapid development of the new energy industry, how to accurately evaluate and predict customer value to meet customer needs better and provide high-quality services has become an important issue the industry faces. To address this issue, we propose an optimization model based on genetic algorithms to improve the value and satisfaction of new energy customers. The model first collects relevant customer data, including historical electricity consumption, electricity consumption behaviour, complaints, etc. Then, we used genetic algorithms to process and analyze these data to find the optimal customer value evaluation model. A genetic algorithm is an optimization algorithm that simulates the evolution process of nature and can automatically search for and optimize solutions to problems. In model optimization, we used simulation experiments to evaluate the effectiveness and performance of different models. A simulation experiment is based on real data, which can simulate actual operations and predict future development trends. Through simulation experiments, we can compare the advantages and disadvantages of different models and select the optimal model for promotion and application.

Keywords: Genetic Algorithm, Customer Value Model, Analysis Simulation Experiment on Optimization of New Energy Customer Value Model Based on Genetic algorithm

DOI:https://doi.org/10.6025/jic/2023/14/3/69-77

Full_Text PDF 948 KB Download: 64 times

References:

[1] Sudira, I. G. N., Hadi, B. K., Moelyadi, M. A., et al. (2016). Application of Genetic Algorithm for the Design Optimization of Geodesic Beam Structure. Applied Mechanics & Materials, 842, 266-272.
[2] Xiao, H., Xu, Z. Z., Kim, L. S., et al. (2015). Optimization scheme of genetic algorithm and its application on aeroengine fault diagnosis. International Journal of Precision Engineering and Manufacturing, 16(4), 735-741.
[3] Corso, L. L., Gasparin, A. L., Martins Gomes, H. (2016). Reliability based design optimization using a genetic algorithm: application to bonded thin films areas of copper/polypropylene. Ingeniare Revista Chilena De Ingeniería, 24(3), 510-519.
[4] Schoonover, P. L., Crossley, W. A., Heister, S. D. (2015). Application of a Genetic Algorithm to the Optimization of Hybrid Rockets. Journal of Spacecraft and Rockets, 37(5), 622-629.
[5] Manu, V. S., Veglia, G. (2016). Optimization of identity operation in NMR spectroscopy via genetic algorithm: Application to the TEDOR experiment. Journal of Magnetic Resonance, 273, 40.
[6] Babatunde, O., Armstrong, L., Diepeenveen, D., et al. (2015). Comparative analysis of Genetic Algorithm and Particle Swarm Optimization: An application in precision agriculture. International Journal of Agricultural Sustainability, 12(1), 71-88.
[7] Adams, L. J., Bello, G., Dumancas, G. G. (2015). Development and Application of a Genetic Algorithm for Variable Optimization and Predictive Modeling of Five-Year Mortality Using Questionnaire Data. Bioinformatics and Biology Insights, 9(Suppl 3), 31- 41.
[8] Davis, J. B. A., Horswell, S. L., Johnston, R. L. (2016). Application of a Parallel Genetic Algorithm to the Global Optimization of Gas-Phase and Supported Gold–Iridium Sub-Nanoalloys. Journal of Physical Chemistry C, 120(7), 224-238.
[9] Jiang, P., Li, X., Dong, Y. (2015). Research and Application of a New Hybrid Forecasting Model Based on Genetic Algorithm Optimization: A Case Study of Shandong Wind Farm in China. Mathematical Problems in Engineering, (2015-1-8), 2015, 215, 1-14.
[10] Lim, T. Y., Al-Betar, M. A., Khader, A. T. (2015). Adaptive pair bonds in genetic algorithm: An application to real-parameter optimization. Applied Mathematics and Computation, 252(C), 503-519.

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